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FAIR Principles:Interpretations and Implementation Considerations

data Intelligence 2020年第1期2卷 10-29,293-302,322页

作者： Annika Jacobsen Ricardo de Miranda Azevedo Nick Juty Dominique Batista Simon Coles Ronald Cornet Melanie Courtot Merce Crosas Michel Dumontier Chris T.Evelo Carole Goble Giancarlo Guizzardi Karsten Kryger Hansen Ali Hasnain Kristina Hettne Jaap Heringa Rob W.W.Hooft Melanie Imming Keith G.Jeffery Rajaram Kaliyaperumal Martijn GKersloot Christine R.Kirkpatrick Tobias Kuhn Ignasi Labastida Barbara Magagna PeterMcQuilton Natalie Meyers Annalisa Montesanti Mirjam van Reisen Philippe Rocca-Serra Robert Pergl Susanna-Assunta Sansone Luiz Olavo Bonino da Silva Santos Juliane Schneider George Strawn Mark Thompson Andra Waagmeester Tobias Weigel Mark D.Wilkinson Egon L.Willighagen Peter Wittenburg Marco Roos Barend Mons Erik Schultes Leiden University Medical Center Leiden2333 ZAThe Netherlands Institute of Data Science Maastricht UniversityUniversiteitssingel 60Maastricht 6229 ERThe Netherlands Department of Computer Science The University of ManchesterOxford RoadManchester M139PLUK Oxford e-Research Centre Department of Engineering SciencesUniversity of OxfordOxford OX13PJUK School of Chemistry Faculty of Engineering and Physical SciencesUniversity of SouthamptonSO171BJUK Amsterdam UMC University of AmsterdamAmsterdam 1000 GGThe Netherlands European Bioinformatics Institute(EMBL-EBI) HinxtonCambridgeCB101SDUK Harvard University CambridgeMassachusetts 02138USA Department of Bioinformatics–BiGCaT NUTRIMMaastricht UniversityMaastricht 6229 ERThe Netherlands Conceptual and Cognitive Modeling Research Group(CORE) Free University of Bozen-BolzanoBolzano 39100Italy Aalborg University Aalborg DK-9220Denmark Insight Centre for Data Analytics National University of Ireland GalwayH91 TK33Ireland Centre for Digital Scholarship Leiden University LibrariesLeiden2333 ZAThe Netherlands Department of Computer Science Vrije Universiteit AmsterdamDe Boelelaan 11051081 HV AmsterdamThe Netherlands Dutch Techcentre for Life Sciences(DTL) UtrechtThe Netherlands SURF Utrecht 3511 EPThe Netherlands Keith G Jeffery Consultants FaringdonUK Castor EDC Paasheuvelweg 25Wing 5D1105 BPAmsterdamThe Netherlands San Diego Supercomputer Center University of California San DiegoLa JollaCalifornia 92093USA Learning and Research Resources Centre(CRAI) Universitat de Barcelona08007 BarcelonaSpain Environment Agency Austria A-1090 ViennaAustria University of Notre Dame 75004 ParisFrance Health Research Board(HRB) Dublin 2DO2 H638Ireland Liacs Institute of Advanced Computer Science Leiden University2311 GJ LeidenThe Netherlands Czech Technical University in Prague Faculty of Information Technology(FIT CTU)16000 Prague 6Czech Republic GO FAIR International Support&Coordination Office(GFISCO) LeidenThe Netherlands Harvard Catalys

The FAIR principles have been widely cited,endorsed and adopted by a broad range of stakeholders since their publication in *** intention,the 15 FAIR guiding principles do not dictate specific technological implementations,but provide guidance for improving Findability,Accessibility,Interoperability and Reusability of digital *** has likely contributed to the broad adoption of the FAIR principles,because individual stakeholder communities can implement their own FAIR ***,it has also resulted in inconsistent interpretations that carry the risk of leading to incompatible ***,while the FAIR principles are formulated on a high level and may be interpreted and implemented in different ways,for true interoperability we need to support convergence in implementation choices that are widely accessible and(re)-*** introduce the concept of FAIR implementation considerations to assist accelerated global participation and convergence towards accessible,robust,widespread and consistent FAIR *** self-identified stakeholder community may either choose to reuse solutions from existing implementations,or when they spot a gap,accept the challenge to create the needed solution,which,ideally,can be used again by other communities in the ***,we provide interpretations and implementation considerations(choices and challenges)for each FAIR principle.

关键词： FAIR guiding principles FAIR implementation FAIR convergence FAIR communities choices and challenges

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ExTra: Transfer-guided exploration

arXiv

引用

arXiv 2019年

作者： Santara, Anirban Ravindran, Balaraman Madan, Rishabh Mitra, Pabitra Department of Computer Science and Engineering IIT Kharagpur Kharagpur WB721302 Robert Bosch Centre for Data Science and AI IIT Madras ChennaiTN600036 Autonomous Ground Vehicle Research Group IIT Kharagpur KharagpurWB721302

In this work we present a novel approach for transfer-guided exploration in reinforcement learning that is inspired by the human tendency to leverage experiences from similar encounters in the past while navigating a new task. Given an optimal policy in a related task-environment, we show that its bisimulation distance from the current task-environment gives a lower bound on the optimal advantage of state-action pairs in the current task-environment. Transfer-guided Exploration (ExTra) samples actions from a Softmax distribution over these lower bounds. In this way, actions with potentially higher optimum advantage are sampled more frequently. In our experiments on gridworld environments, we demonstrate that given access to an optimal policy in a related task-environment, ExTra can outperform popular domain-specific exploration strategies viz. epsilon greedy, Model-Based Interval Estimation - Exploration Based (MBIE-EB), Pursuit and Boltzmann in terms of sample complexity and rate of convergence. We further show that ExTra is robust to choices of source task and shows a graceful degradation of performance as the dissimilarity of the source task increases. We also demonstrate that ExTra, when used alongside traditional exploration algorithms, improves their rate of convergence. Thus it is capable of complimenting the efficacy of traditional exploration algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Reinforcement learning

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Deluge Harmony Search Algorithm For Nurse Rostering Problems

Deluge Harmony Search Algorithm For Nurse Rostering Problems

引用

First International Conference of Intelligent Computing and engineering (ICOICE)

作者： Mohammed Hadwan Masri Ayob Murad A. Rassam Essa A. Hezam Faculty of Applied Sciences Taiz University Taiz Yemen Data Mining and Optimisation Research Group (DMO) Centre for Artificial Intelligent (CAIT) Universiti Kebangsaan Malaysia Selangor Malaysia College of Computer Science and Engineering Taibah University Madinah Saudi Arabia

ISBN: (数字)9781728144870

ISBN: (纸本)9781728144887

Harmony search algorithm (HSA) is one of the relatively new metaheuristic algorithms that classified under population-based search algorithms. Based on literature, hybridizing local-based searching algorithms with population-based algorithms can improve the performance of hybridized algorithms. This research is an extension to our previous work that focus on solving Nurse Rostering Problems (NRP) using hybrid metaheuristic algorithms. One of the improved version of HSA is enhanced harmony search algorithm (EHSA) where it overcomes some of the weaknesses of basic HSA. Slow convergence is noticed in EHSA which encourage us to hybridize it with other metaheuristic algorithms to improve its performance. In this research, EHSA is hybridized with great deluge algorithm (GD) and called Deluged harmony search algorithm (DHSA). DHSA then compared to CHSA (the hybridization of EHSA with Hill climbing (HC)) which developed earlier. To strike the balance between exploration and exploitation, the exploration stage run using EHSA and the exploitation stage used GD. DHSA is tested to solve a real world NRP problem at National University Malaysia Medical Center (UKMMC). The results show that, DHSA performed much better than CHSA in all instances in terms of solution quality with slightly higher execution time.

关键词： Classification algorithms Search problems Medical services Information technology Simulated annealing Genetic algorithms

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Preface

EAI/Springer Innovations in Communication and Computing

引用

EAI/Springer Innovations in Communication and Computing 2022年 vii-viii页

作者： Johri, Prashant Diván, Mario José Khanam, Ruqaiya Marciszack, Marcelo Will, Adrián Yamuna Expressway School of Computing Science & Engineering Galgotias University G.B.Nagar Plot No 2 Sector 17 A Greater Noida Uttar Pradesh India Computer Science and Engineering Department SET Sharda University Greater Noida Uttar Pradesh India Information System Department National Technological University-Tucumán Regional Faculty Tucumán Tucumán Argentina Data Science Research Group at Economy School National University of La Pampa Santa Rosa Argentina National Technological University-Córdoba Regional Faculty Córdoba Argentina

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Social recommender system: A recommender system based on tweets for points of interest 17

Social recommender system: A recommender system based on twe...

引用

4th Multidisciplinary International Social Networks Conference, MISNC 2017

作者： García, Cristian González Meana-Llorián, Daniel García-Díaz, Vicente Núñez-Valdez, Edward Rolando Model-Driven Engineering Research Group Department of Computer Science Oviedo University of Oviedo Asturias Spain

ISBN: (纸本)9781450348812

Social networks are an inexhaustible source of knowledge. One of its main uses is to express opinions about a particular product or service. Thus, users usually write opinions about places or so-called points of interest. That is a valuable knowledge for other users and especially for users who know and trust the opinions of the writers. In this work, we present an overview of a platform to capture opinions related to places and generate a positive or a negative recommendation for users based on both the global use of Twitter as well as the comments made by trusted people on the network. © 2017 ACM.

关键词： Social networking (online)

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An Early Stage researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

引用

Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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Using mapping languages for building legal knowledge graphs from XML files?

arXiv

引用

arXiv 2019年

作者： Junior, Ademar Crotti Orlandi, Fabrizio O’Sullivan, Declan Dirschl, Christian Reul, Quentin ADAPT Centre for Digital Content Platform Research Knowledge & Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Dublin 2 Ireland Wolters Kluwer Deutschland Munchen Germany Wolters Kluwer N.V. Chicago United States

This paper presents our experience on building RDF knowledge graphs for an industrial use case in the legal domain. The information contained in legal information systems are often accessed through simple keyword interfaces and presented as a simple list of hits. In order to improve search accuracy one may avail of knowledge graphs, where the semantics of the data can be made explicit. Significant research effort has been invested in the area of building knowledge graphs from semi-structured text documents, such as XML, with the prevailing approach being the use of mapping languages. In this paper, we present a semantic model for representing legal documents together with an industrial use case. We also present a set of use case requirements based on the proposed semantic model, which are used to compare and discuss the use of state-of-the-art mapping languages for building knowledge graphs for legal data. Copyright © 2019, The Authors. All rights reserved.

关键词： Mapping

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Unsupervised Keyword Extraction Method Based on Chinese Patent Clustering

Unsupervised Keyword Extraction Method Based on Chinese Pate...

引用

IEEE International Conference on Big Knowledge (ICBK)

作者： Yuxin Xie Xuegang Hu Yuhong Zhang Shi Li Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China Intelligent Connected Vehicle Research Institute (JAC Technical Center) Anhui Jianghuai Automobile Group Corp.Ltd Hefei China

Recently, patent data analysis has attracted a lot of attention, and patent keyword extraction is a hot problem. Most existing methods for patent keyword extraction are based on the frequency of words without semantic information. In this paper, we propose an Unsupervised Keyword Extraction Method (UKEM) based on Chinese patent clustering. More specifically, we use a Skip-gram model to train word embeddings based on a Chinese patent corpus. Then each patent is represented as a vector called patent vector. These patent vectors are clustered to obtain several cluster centroids. Next, the distance between each word vector in patent abstract and cluster centroid is computed to indicate the semantic importance of this word. The experimental results on several Chinese patent datasets show that the performance of our proposed method is better than several competitive methods.

关键词： Patents Semantics data mining Knowledge engineering Neural networks Big data computer science

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Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images

arXiv

引用

arXiv 2020年

作者： Chatterjee, Soumick Saad, Fatima Sarasaen, Chompunuch Ghosh, Suhita Krug, Valerie Khatun, Rupali Mishra, Rahul Desai, Nirja Radeva, Petia Rose, Georg Stober, Sebastian Speck, Oliver Nürnberger, Andreas Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Faculty of Computer Science Otto von Guericke University Magdeburg Germany Genomics Research Centre Human Technopole Italy Institute for Medical Engineering Otto von Guericke University Magdeburg Germany Research Campus STIMULATE Otto von Guericke University Magdeburg Germany Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Germany Artificial Intelligence Lab Otto von Guericke University Magdeburg Germany Department of Mathematics and Computer Science University of Barcelona Barcelona Spain Translational Radiobiology Department of Radiation Oncology Universitätsklinikum Erlangen Erlangen Germany Apollo Hospitals Bilaspur India HCG Cancer Centre Vadodara India Computer Vision Centre Cerdanyola Barcelona Spain Centre for Behavioural Brain Sciences Magdeburg Germany German Centre for Neurodegenerative Diseases Magdeburg Germany

The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosing infected patients. Medical imaging, such as X-ray and Computed Tomography (CT), combined with the potential of Artificial Intelligence (AI), plays an essential role in supporting medical personnel in the diagnosis process. Thus, in this article five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2 and DenseNet161) and their ensemble, using majority voting have been used to classify COVID-19, pneumoniæ and healthy subjects using chest X-ray images. Multilabel classification was performed to predict multiple pathologies for each patient, if present. Firstly, the interpretability of each of the networks was thoroughly studied using local interpretability methods - occlusion, saliency, input X gradient, guided backpropagation, integrated gradients, and DeepLIFT, and using a global technique - neuron activation profiles. The mean Micro-F1 score of the models for COVID-19 classifications ranges from 0.66 to 0.875, and is 0.89 for the ensemble of the network models. The qualitative results showed that the ResNets were the most interpretable models. This research demonstrates the importance of using interpretability methods to compare different models before making a decision regarding the best performing model. Copyright © 2020, The Authors. All rights reserved.

关键词： COVID-19

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Analyzing the Structure of Attention in a Transformer Language Model

arXiv

引用

arXiv 2019年

作者： Vig, Jesse Belinkov, Yonatan Palo Alto Research Center Machine Learning and Data Science Group Interaction and Analytics Lab Palo AltoCA United States Harvard John A. Paulson School of Engineering and Applied Sciences MIT Computer Science and Artificial Intelligence Laboratory CambridgeMA United States

The Transformer is a fully attention-based alternative to recurrent networks that has achieved state-of-the-art results across a range of NLP tasks. In this paper, we analyze the structure of attention in a Transformer language model, the GPT-2 small pretrained model. We visualize attention for individual instances and analyze the interaction between attention and syntax over a large corpus. We find that attention targets different parts of speech at different layer depths within the model, and that attention aligns with dependency relations most strongly in the middle layers. We also find that the deepest layers of the model capture the most distant relationships. Finally, we extract exemplar sentences that reveal highly specific patterns targeted by particular attention heads. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

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